ISPRS Open Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Transfer learning and single-polarized SAR image preprocessing for oil spill detection","authors":"Nataliia Kussul ,&nbsp;Yevhenii Salii ,&nbsp;Volodymyr Kuzin ,&nbsp;Bohdan Yailymov ,&nbsp;Andrii Shelestov","doi":"10.1016/j.ophoto.2024.100081","DOIUrl":"10.1016/j.ophoto.2024.100081","url":null,"abstract":"<div><div>This study addresses the challenge of oil spill detection using Synthetic Aperture Radar (SAR) satellite imagery, employing deep learning techniques to improve accuracy and efficiency. We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extraction from single-channel SAR data to improve oil spill detection performance.</div><div>Our methodology involved developing a novel preprocessing pipeline that converts single-channel SAR data into a three-channel RGB representation. The preprocessing technique normalizes SAR intensity values and encodes extracted features into RGB channels.</div><div>Through an experiment, we have shown that a combination of the LinkNet with an EfficientNet-B4 is superior to pairs of other well-known architectures and encoders.</div><div>Quantitative evaluation revealed a significant improvement in F1-score of 0.064 compared to traditional dB-scale preprocessing methods. Qualitative assessment on independent SAR scenes from the Mediterranean Sea demonstrated better detection capabilities, albeit with increased sensitivity to look-alike.</div><div>We conclude that our proposed preprocessing technique shows promise for enhancing automatic oil spill segmentation from SAR imagery. The study contributes to advancing oil spill detection methods, with potential implications for environmental monitoring and marine ecosystem protection.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new unified framework for supervised 3D crown segmentation (TreeisoNet) using deep neural networks across airborne, UAV-borne, and terrestrial laser scans","authors":"Zhouxin Xi,&nbsp;Dani Degenhardt","doi":"10.1016/j.ophoto.2025.100083","DOIUrl":"10.1016/j.ophoto.2025.100083","url":null,"abstract":"<div><div>Accurately defining and isolating 3D tree space is critical for extracting and analyzing tree inventory attributes, yet it remains a challenge due to the structural complexity and heterogeneity within natural forests. This study introduces TreeisoNet, a suite of supervised deep neural networks tailored for robust 3D tree segmentation across natural forest environments. These networks are specifically designed to identify tree locations, stem components (if available), and crown clusters, making them adaptable to varying scales of laser scanning from airborne laser scannner (ALS), terrestrial laser scanner (TLS), and unmanned aerial vehicle (UAV). Our evaluation used three benchmark datasets with manually isolated tree references, achieving mean intersection-over-union (mIoU) accuracies of 0.81 for UAV, 0.76 for TLS, and 0.59 for ALS, which are competitive with contemporary algorithms such as ForAINet, Treeiso, Mask R-CNN, and AMS3D. Noise from stem point delineation minimally impacted stem location detection but significantly affected crown clustering. Moderate manual refinement of stem points or tree centers significantly improved tree segmentation accuracies, achieving 0.85 for UAV, 0.86 for TLS, and 0.80 for ALS. The study confirms SegFormer as an effective 3D point-level classifier and an offset-based UNet as a superior segmenter, with the latter outperforming unsupervised solutions like watershed and shortest-path methods. TreeisoNet demonstrates strong adaptability in capturing invariant tree geometry features, ensuring transferability across different resolutions, sites, and sensors with minimal accuracy loss.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting and measuring fine-scale urban tree canopy loss with deep learning and remote sensing","authors":"David Pedley,&nbsp;Justin Morgenroth","doi":"10.1016/j.ophoto.2025.100082","DOIUrl":"10.1016/j.ophoto.2025.100082","url":null,"abstract":"<div><div>Urban trees provide a multitude of environmental and amenity benefits for city occupants yet face ongoing risk of removal due to urban pressures and the preferences of landowners. Understanding the extent and location of canopy loss is critical for the effective management of urban forests. Although city-scale assessments of urban forest canopy cover are common, the accurate identification of fine-scale canopy loss remains challenging. Evaluating change at the property scale is of particular importance given the localised benefits of urban trees and the scale at which tree removal decisions are made.</div><div>The objective of this study was to develop a method to accurately detect and quantify the city-wide loss of urban tree canopy (UTC) at the scale of individual properties using publicly available remote sensing data. The study area was the city of Christchurch, New Zealand, with the study focussed on UTC loss that occurred between 2016 and 2021. To accurately delineate the 2016 UTC, a semantic segmentation deep learning model (DeepLabv3+) was pretrained using existing UTC data and fine-tuned using high resolution aerial imagery. The output of this model was then segmented into polygons representing individual trees using the Segment Anything Model. To overcome poor alignment of aerial imagery, LiDAR point cloud data was utilised to identify changes in height between 2016 and 2021, which was overlaid across the 2016 UTC to map areas of UTC loss. The accuracy of UTC loss predictions was validated using a visual comparison of aerial imagery and LiDAR data, with UTC loss quantified for each property within the study area.</div><div>The loss detection method achieved accurate results for the property-scale identification of UTC loss, including a mean F1 score of 0.934 and a mean IOU of 0.883. Precision values were higher than recall values (0.941 compared to 0.811), which reflected a deliberately conservative approach to avoid false positive detections. Approximately 14.5% of 2016 UTC was lost by 2021, with 74.9% of the UTC loss occurring on residential land. This research provides a novel geospatial method for evaluating fine-scale city-wide tree dynamics using remote sensing data of varying type and quality with imperfect alignment. This creates the opportunity for detailed evaluation of the drivers of UTC loss on individual properties to enable better management of existing urban forests.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of ultra-wideband positioning for measuring tree positions in boreal forest plots","authors":"Zuoya Liu ,&nbsp;Harri Kaartinen ,&nbsp;Teemu Hakala ,&nbsp;Heikki Hyyti ,&nbsp;Juha Hyyppä ,&nbsp;Antero Kukko ,&nbsp;Ruizhi Chen","doi":"10.1016/j.ophoto.2025.100087","DOIUrl":"10.1016/j.ophoto.2025.100087","url":null,"abstract":"<div><div>Accurate individual tree locations enable efficient forest inventory management and automation, support precise forest surveys, management decisions and future individual-tree harvesting plans. In this paper, we compared and analyzed in detail the performance of an ultra-wideband (UWB) data-driven method for mapping individual tree locations in boreal forest sample plots of varying complexity. Twelve forest sample plots selected from varying forest-stand conditions representing different developing stages, stem densities and abundance of sub canopy growth in boreal forests were tested. These plots were classified into three categories (“Easy”, “Medium” and “Difficult”) according to these varying stand conditions. The experimental results show that UWB data-driven method is able to map individual tree locations accurately with total root-mean-squared-errors (RMSEs) of 0.17 m, 0.2 m, and 0.26 m for “Easy”, “Medium” and “Difficult” forest plots, respectively, providing a strong reference for forest surveys.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intensity-based stochastic model of terrestrial laser scanners: Methodological workflow, empirical derivation and practical benefit","authors":"Florian Schill ,&nbsp;Christoph Holst ,&nbsp;Daniel Wujanz ,&nbsp;Jens Hartmann ,&nbsp;Jens-André Paffenholz","doi":"10.1016/j.ophoto.2024.100079","DOIUrl":"10.1016/j.ophoto.2024.100079","url":null,"abstract":"<div><div>After more than twenty years of commercial use, laser scanners have reached technical maturity and consequently became a standard tool for 3D-data acquisition across various fields of application. Yet, meaningful stochastic information regarding the achieved metric quality of recorded points remains an open research question. Recent research demonstrated that raw intensity values can be deployed to derive stochastic models for reflectorless rangefinders. Yet, all existing studies focused on single instances of particular laser scanners while the derivation of the stochastic models required significant efforts.</div><div>Motivated by the aforementioned shortcomings, the focus of this study is set on the comparison of stochastic models for a series of eight identical phase-based scanners that differ in age, working hours and date of last calibration. In order to achieve this, a standardised methodological workflow is suggested to derive the unknown parameters of the individual stochastic models. Based on the generated outcome, a comparison is conducted which clarifies if a universally applicable stochastic model (type calibration) can be used for a particular scanner model or if individual parameter sets are still required for every scanner (instance calibration) to validate the practical benefit and usability of those models. The generated results successfully demonstrate that the computed stochastic model is transferable to all individual scanners of the series.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plant trait retrieval from hyperspectral data: Collective efforts in scientific data curation outperform simulated data derived from the PROSAIL model","authors":"Daniel Mederer ,&nbsp;Hannes Feilhauer ,&nbsp;Eya Cherif ,&nbsp;Katja Berger ,&nbsp;Tobias B. Hank ,&nbsp;Kyle R. Kovach ,&nbsp;Phuong D. Dao ,&nbsp;Bing Lu ,&nbsp;Philip A. Townsend ,&nbsp;Teja Kattenborn","doi":"10.1016/j.ophoto.2024.100080","DOIUrl":"10.1016/j.ophoto.2024.100080","url":null,"abstract":"<div><div>Plant traits play a pivotal role in steering ecosystem dynamics. As plant canopies have evolved to interact with light, spectral data convey information on a variety of plant traits. Machine learning techniques have been used successfully to retrieve diverse traits from hyperspectral data. Nonetheless, the efficacy of machine learning is restricted by limited access to high-quality reference data for training. Previous studies showed that aggregating data across domains, sensors, or growth forms provided by collaborative efforts of the scientific community enables the creation of transferable models. However, even such curated databases are still sparse for several traits. To address these challenges, we investigated the potential of filling such data gaps with simulated hyperspectral data generated through the most widely-used radiative transfer model (RTM) PROSAIL. We coupled trait information from the TRY plant trait database with information on plant communities from the sPlot database, to build a realistic input trait dataset for the RTM-based simulation of canopy spectra. Our findings indicate that simulated data can alleviate the effects of data scarcity for highly underrepresented traits. In most other cases, however, the effects of including simulated data from RTMs are negligible or even negative. While more complex RTM models promise further improvements, their parameterization remains challenging. This highlights two key observations: firstly, RTM models, such as PROSAIL, exhibit limitations in producing realistic spectra across diverse ecosystems; secondly, real-world data repurposed from various sources exhibit superior retrieval success compared to simulated data. As a result, we advocate to emphasize the importance of active data sharing over secrecy and overreliance on modeling to address data limitations.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100080"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral unmixing with spatial context and endmember ensemble learning with attention mechanism","authors":"R.M.K.L. Ratnayake,&nbsp;D.M.U.P. Sumanasekara,&nbsp;H.M.K.D. Wickramathilaka,&nbsp;G.M.R.I. Godaliyadda,&nbsp;H.M.V.R. Herath,&nbsp;M.P.B. Ekanayake","doi":"10.1016/j.ophoto.2025.100086","DOIUrl":"10.1016/j.ophoto.2025.100086","url":null,"abstract":"<div><div>In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. Most of these networks use an Endmember Extraction Algorithm(EEA) for the initialization of their network. As EEAs performance depends on the environment, single initialization does not ensure optimum performance. Also, only a few networks utilize the spatial context in HS Images to solve the unmixing problem. In this paper, we propose Hyperspectral Unmixing with Spatial Context and Endmember Ensemble Learning with Attention Mechanism (SCEELA) to address these issues. The proposed method has three main components, Signature Predictor (SP), Pixel Contextualizer (PC) and Abundance Predictor (AP). SP uses an ensemble of EEAs for each endmember as the initialization and the attention mechanism within the transformer enables ensemble learning to predict accurate endmembers. The attention mechanism in the PC enables the network to capture the contextual data and provide a more refined pixel to the AP to predict the abundance of that pixel. SCEELA was compared with eight state-of-the-art HS unmixing algorithms for three widely used real datasets and one synthetic dataset. The results show that the proposed method shows impressive performance when compared with other state-of-the-art algorithms.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Deep Learning models for mapping rice cultivation area in Bhutan using high-resolution satellite imagery","authors":"Biplov Bhandari,&nbsp;Timothy Mayer","doi":"10.1016/j.ophoto.2025.100084","DOIUrl":"10.1016/j.ophoto.2025.100084","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Crop type and crop extent are critical information that helps policymakers make informed decisions on food security. As the economic growth of Bhutan has increased at an annual rate of 7.5% over the last three decades, there is a need to provide geospatial products that can be leveraged by local experts to support decision-making in the context of economic and population growth effects and impacts on food security. To address these concerns related to food security, through various policies and implementation, the Bhutanese government is promoting several drought-resilient, high-yielding, and disease-resistant crop varieties to actively combat environmental challenges and support higher crop yields. Simultaneously the Bhutanese government is increasing its utilization of technological approaches such as including Remote Sensing-based knowledge and data products into their decision-making process. This study focuses on Paro, one of the top rice-yielding districts in Bhutan, and employs publicly available Norway’s International Climate and Forest Initiative (NICFI) high-resolution satellite imagery from Planet Labs. Two Deep Learning approaches, point-based (DNN) and patch-based (U-Net), models were used in conjunction with cloud-computing platforms. Four different models per Deep Learning approaches (DNN and U-Net) were trained: (1) Red, Green, Blue, and Near-Infrared (RGBN) channels from Planet, (2) RGBN and Elevation data (RGBNE), (3) RGBN and Sentinel-1 data (RGBNS), and (4) RGBN with Elevation and Sentinel-1 data (RGBNES). From this comprehensive analysis, the U-Net displayed higher performance metrics across both model training and model validation efforts. Among the U-Net model sets, the RGBN, RGBNE, RGBNS, and RGBNES models had an F1-score of 0.8546, 0.8563, 0.8467, and 0.8500 respectively. An additional independent model evaluation was performed and found a high level of performance variation across all the metrics (precision, recall, and F1-score) underscoring the need for practitioners to employ independent validation. For this independent model evaluation, the U-Net-based RGBN, RGBNE, RGBNS, and RGBNES models displayed the F1-scores of 0.5935, 0.6154, 0.5882, and 0.6582, suggesting U-Net RGBNES as the best model across the comparison. The study demonstrates that the Deep Learning approaches can be used for mapping rice cultivation area, and can also be used in combination with the survey-based approaches currently utilized by the Department of Agriculture (DoA) in Bhutan. Further this study successfully demonstrated the usage of regional land cover products such as SERVIR’s Regional Land Cover Monitoring System (RLCMS) as a weak label approach to capture different strata addressing the class imbalance problem and improving the sampling design for Deep Learning application. Finally, through preliminary model testing and comparisons outlined it was demonstrated that using additional features such as NDVI, EVI, and NDWI did not d","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimal rotations in arbitrary dimensions with applications to hypothesis testing and parameter estimation","authors":"Jochen Meidow,&nbsp;Horst Hammer","doi":"10.1016/j.ophoto.2025.100085","DOIUrl":"10.1016/j.ophoto.2025.100085","url":null,"abstract":"<div><div>The rotation of a vector around the origin and in a plane constitutes a minimal rotation. Such a rotation is of vital importance in many applications. Examples are the re-orientation of spacecraft or antennas with minimal effort, the smooth interpolation between sensor poses, and the drawing of circular arcs in 2D and 3D. In numerical linear algebra, minimal rotations in different planes are used to manipulate matrices, e.g., to compute the QR decomposition of a matrix. This review compiles the concepts and formulas for minimal rotations in arbitrary dimensions for easy reference and provides a summary of the mathematical background necessary to understand the techniques described in this paper. The discussed concepts are accompanied by important examples in the context of photogrammetric image analysis. Hypothesis testing and parameter estimation for uncertain geometric entities are described in detail. In both applications, minimal rotations play an important role.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees","authors":"Mitch Bryson ,&nbsp;Ahalya Ravendran ,&nbsp;Celine Mercier ,&nbsp;Tancred Frickey ,&nbsp;Sadeepa Jayathunga ,&nbsp;Grant Pearse ,&nbsp;Robin J.L. Hartley","doi":"10.1016/j.ophoto.2024.100078","DOIUrl":"10.1016/j.ophoto.2024.100078","url":null,"abstract":"<div><div>Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications and environments. One of the main downsides of deep learning-based approaches is the need for extensive training datasets, <em>i.e</em>. LiDAR point clouds that have been annotated for target tasks by human experts. One strategy for addressing this issue is the use of simulated/synthetic data (with automatically generated annotations) for training models which can then be deployed on real target data/environments. This paper explores using synthetic data of realistic forest trees to train deep learning models for tree part segmentation from real forest LiDAR data. We develop a new pipeline for generating high-fidelity simulated LiDAR scans of synthetic forest trees and combine this with an unsupervised domain adaptation strategy to adapt models trained on synthetic data to LiDAR data captured in real forest environments.</div><div>Models were trained for semantic segmentation of tree parts using a PointNet++ architecture and evaluated across a range of medium to high-resolution laser scanning datasets collected across both ground-based and aerial platforms in multiple forest environments. Results of our work indicated that models trained on our synthetic data pipeline were competitive with models trained on real data, in particular when real data came from non-target sites, and our unsupervised domain adaptation method further improved performance. Our approach has implications for reducing the burden required in manual human expert annotation of large LiDAR datasets required to achieve high-performance from deep learning methods for forest analysis. The use of synthetically-trained models shown here provides a potential way to reduce the barriers to the use of deep learning in large-scale forest analysis, with implications to applications ranging from forest inventories to scaling-up in-situ forest phenotyping.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"14 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}